# DoubleML Treatment Heterogeneity

Tools for Causality
Grenoble, Sept 25 - 29, 2023
Philipp Bach, Sven Klaassen

# Motivation

## Motivation

• In many cases, treatment effects are heterogeneous across individuals

• Think of the effect of a training program on wages:

• The effect is likely to be different for individuals who participate in the program and those who do not
• Usually, the goal is not to force everyone to do training programs
• Heterogeneity is of interest in many applications:

• Effect of a marketing campaign on different types of customers (uplift modeling, e.g., different markets, different age groups, etc.)
• Effect of a drug on different types of patients (e.g. age groups)

# Treatment Effect on the Treated

## Treatment Effect on the Treated

• The Average Treatment Effect on the Treated (ATTE) is defined as the effect of the treatment for the subpopulation of individuals who actually receive the treatment

$\tau_{ATTE} = E[Y(1) - Y(0)| D = 1]$

## Treatment Effect on the Treated

• The ATTE for DoubleMLIRM models can be estimated by adjusting the score parameter to score="ATTE"
Code
from doubleml import DoubleMLData
from doubleml.datasets import fetch_401K

data = fetch_401K(return_type='DataFrame')

# Construct DoubleMLData object
dml_data = DoubleMLData(data,
y_col='net_tfa',
d_cols='e401',
x_cols=['age', 'inc', 'educ', 'fsize', 'marr',
'twoearn', 'db', 'pira', 'hown'])
from doubleml import DoubleMLIRM
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

dml_irm_atte = DoubleMLIRM(dml_data,
ml_g = RandomForestRegressor(),
ml_m = RandomForestClassifier(),
score="ATTE")

_ = dml_irm_atte.fit()
dml_irm_atte.summary.round(3)
coef std err t P>|t| 2.5 % 97.5 %
e401 10656.592 2419.86 4.404 0.0 5913.753 15399.431

## GATEs

• Often, we are interested in different subpopulations of individuals

• Group Average Treatment Effects (GATEs) are defined as the average treatment effect for a subpopulation of individuals (defined via an indicator $G$)

$\tau_{GATE} = E[Y(1) - Y(0)| G = 1]$

• Typical applications include:
• Customer segmentation

## GATEs

• For DoubleMLIRM models, the GATEs can be estimated based on a standard model (score="ATE") by using the gate() method

• Estimation of GATEs does not require re-estimation of the model

from doubleml import DoubleMLIRM
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

dml_irm= DoubleMLIRM(dml_data,
ml_g = RandomForestRegressor(),
ml_m = RandomForestClassifier(),
score="ATE")

_ = dml_irm.fit()
dml_irm.summary.round(3)
coef std err t P>|t| 2.5 % 97.5 %
e401 8919.856 1594.868 5.593 0.0 5793.972 12045.739

## GATEs

• At first, we have to define groups and a corresponding pandas dataframe
import pandas as pd
groups = pd.DataFrame({'married': dml_data.data['marr']})
groups.head(n=3)
married
0 0
1 0
2 0
• Then, we can call the gate() method and construct confidence intervals with the confint() method
import numpy as np
gate = dml_irm.gate(groups)
np.random.seed(123)
print(gate.confint(level=0.95, joint = True))
               2.5 %        effect        97.5 %
married  3605.212609  10786.481802  17967.750994

## GATEs

• If we want to consider different groups, we could either call gate() multiple times or define the groups in a single dataframe
groups = pd.DataFrame({'married': dml_data.data['marr'] == 1,
'not_married': dml_data.data['marr'] == 0})
groups.head(n=3)
married not_married
0 False True
1 False True
2 False True
• This enables valid uniform confidence intervals
gate = dml_irm.gate(groups)
np.random.seed(123)
print(gate.confint(level=0.95, joint = True))
                   2.5 %        effect        97.5 %
married      2886.213331  10786.481802  18686.750272
not_married -1653.828602   6062.745725  13779.320052

## GATEs

• Important: For valid confidence intervals, the groups need to be mutually exclusive!

• A simple and intuitive way to set up mutually exclusive groups is to use only one column to define the different groups

marriage_status = dml_data.data['marr'].replace({1: 'married', 0: 'not married'})
groups = pd.DataFrame({'marriage_status': marriage_status})
groups.head(n=3)
marriage_status
0 not married
1 not married
2 not married
gate = dml_irm.gate(groups)
print(gate.confint(level=0.95))
                         2.5 %        effect        97.5 %
Group_married      6430.310537  10786.481802  15142.653066
Group_not married  1807.862556   6062.745725  10317.628894

## CATEs

• Conditional Average Treatment Effects (CATEs) are defined as the average treatment effect for a subpopulation of individuals (defined via a vector of covariates $X$)

$\tau(x) = E[Y(1) - Y(0)| X = x]$

• Depending on $X$ this can be quite complicated to estimate (e.g. if $X = (X_1, X_2, X_3, \dots)$ includes multiple variables)

• A very simple idea is to approximate $\tau(x)$ as a linear function of $X$ (see Semenova and Chernozhukov (2021)):

$\tau(x) \approx \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots$

• Note that the coefficients do not have a causal interpretation! This does not mean $X$ affects $D$ or $D$ affects $X$!

## Basic CATEs

• Consider the conditional average effect of age
age_data = dml_data.data["age"]
linear_basis = pd.DataFrame({'intercept': np.ones_like(age_data),
'age': age_data})
cate_linear = dml_irm.cate(linear_basis)
print(cate_linear)
================== DoubleMLBLP Object ==================

------------------ Fit summary ------------------
coef      std err         t     P>|t|        [0.025  \
intercept -2659.518816  6528.150481 -0.407392  0.683729 -15456.021077
age         282.009614   154.172673  1.829180  0.067403    -20.200171

0.975]
intercept  10136.983446
age          584.219400  

## Basic CATEs

• Usually, we are not interested in the coefficients (or confidence intervals), but confidence values for new observations
new_data = {"age": np.linspace(np.quantile(age_data, 0.2), np.quantile(age_data, 0.8), 50)}
linear_grid = pd.DataFrame({"intercept": np.ones_like(new_data["age"]),
"age": new_data["age"]})
df_cate_linear = cate_linear.confint(linear_grid, level=0.95, joint=True, n_rep_boot=2000)
print(df_cate_linear.head(n=8))
         2.5 %       effect        97.5 %
0  -387.913346  6082.779233  12553.471813
1  -130.121913  6197.885199  12525.892310
2   121.537870  6312.991164  12504.444457
3   366.651582  6428.097129  12489.542676
4   604.789274  6543.203094  12481.616914
5   835.508734  6658.309059  12481.109383
6  1058.359741  6773.415024  12488.470307
7  1272.889368  6888.520989  12504.152610

## Basic CATEs

• This can be easily visualized
Code
df_cate_linear_pointwise = cate_linear.confint(linear_grid, level=0.95, joint=False)

import matplotlib.pyplot as plt
df_cate_linear['age'] = new_data['age']
fig, ax = plt.subplots()
_ = ax.grid(visible=True)
_ = ax.plot(df_cate_linear['age'],df_cate_linear['effect'], color='violet', label='Estimated Effect')
_ = ax.fill_between(df_cate_linear['age'], df_cate_linear['2.5 %'], df_cate_linear['97.5 %'], color='violet', alpha=.3, label='Joint Confidence Interval')
_ = ax.fill_between(df_cate_linear['age'], df_cate_linear_pointwise['2.5 %'], df_cate_linear_pointwise['97.5 %'], color='violet', alpha=.5, label='Pointwise Confidence Interval')

_ = plt.legend()
_ = plt.title('CATE')
_ = plt.xlabel('age')
_ = plt.ylabel('Effect and 95%-CI')
plt.show()

## Polynomial CATE Approximations

• Since the linear approximation is quite restrictive, we can also use a more flexible approximation, e.g. polynomial

$\tau(x)\approx \beta_0 + \beta_1 x_1 + \beta_2 x_1^2 + \dots$

from sklearn.preprocessing import PolynomialFeatures

# Create the polynomial features object
poly = PolynomialFeatures(degree=3)
poly_basis_array = poly.fit_transform(dml_data.data[["age"]])
poly_basis = pd.DataFrame(poly_basis_array, columns=poly.get_feature_names_out())
print(poly_basis.head())
     1   age   age^2     age^3
0  1.0  47.0  2209.0  103823.0
1  1.0  36.0  1296.0   46656.0
2  1.0  37.0  1369.0   50653.0
3  1.0  58.0  3364.0  195112.0
4  1.0  32.0  1024.0   32768.0

## Polynomial CATE Approximations

• Now, we can estimate the CATEs based on the polynomial basis
cate_poly = dml_irm.cate(poly_basis)
print(cate_poly)
================== DoubleMLBLP Object ==================

------------------ Fit summary ------------------
coef        std err         t     P>|t|         [0.025  \
1     -156771.428293  110574.481330 -1.417790  0.156283 -373519.899306
age     11501.004758    8119.848867  1.416406  0.156688   -4415.550361
age^2    -260.747500     191.922980 -1.358605  0.174303    -636.955572
age^3       1.942806       1.462744  1.328193  0.184145      -0.924469

0.975]
1      59977.042721
age    27417.559878
age^2    115.460573
age^3      4.810081  

## Polynomial CATE Approximations

• Create a regular grid of new observations
poly_grid = pd.DataFrame(poly.transform(new_data["age"].reshape(-1,1)),
columns=poly.get_feature_names_out())
df_cate_poly = cate_poly.confint(poly_grid, level=0.95, joint=True, n_rep_boot=2000)
print(df_cate_poly.head(n=8))
         2.5 %       effect        97.5 %
0 -1371.784065  7059.496867  15490.777800
1 -1305.121742  7428.236489  16161.594719
2 -1240.794431  7771.091021  16782.976473
3 -1172.562904  8088.853116  17350.269136
4 -1096.322779  8382.315426  17860.953631
5 -1009.553305  8652.270602  18314.094509
6  -910.920448  8899.511296  18709.943039
7  -799.993175  9124.830159  19049.653492

## Polynomial CATE Approximations

• And visualize the results
Code
df_cate_poly_pointwise = cate_poly.confint(poly_grid, level=0.95, joint=False)

import matplotlib.pyplot as plt
df_cate_poly['age'] = new_data['age']
fig, ax = plt.subplots()
_ = ax.grid(visible=True)
_ = ax.plot(df_cate_poly['age'],df_cate_poly['effect'], color='violet', label='Estimated Effect')
_ = ax.fill_between(df_cate_poly['age'], df_cate_poly['2.5 %'], df_cate_poly['97.5 %'], color='violet', alpha=.3, label='Joint Confidence Interval')
_ = ax.fill_between(df_cate_poly['age'], df_cate_poly_pointwise['2.5 %'], df_cate_poly_pointwise['97.5 %'], color='violet', alpha=.5, label='Pointwise Confidence Interval')

_ = plt.legend()
_ = plt.title('CATE')
_ = plt.xlabel('age')
_ = plt.ylabel('Effect and 95%-CI')
plt.show()

## Policy Learning with Trees

• The DoubleML package also supports basic policy learning with trees for the DoubleMLIRM model (similar to Athey and Wager (2021))

• General reasoning: If we know that the treatment effect is positive for some observations and negative for others, why don’t we base our treatment assignment on that knowledge?

• Idea: Use a classification tree to optimize over feature regions

• Let $X$ be the set of covariates for which we want to optimize the treatment assignment with a policy

$\pi: X \rightarrow \{0,1\}$

## Policy Learning with Trees

• The policy is then defined by minimizing the following objective function

$\frac{1}{n}\sum_{i=1}^n \underbrace{\left(2\pi(X_i) - 1\right)}_{\text{policy decision}} \underbrace{\psi_b(W_i, \hat{\eta})}_{\text{effect size}} = \frac{1}{n}\sum_{i=1}^n \left(2\pi(X_i) - 1\right) \underbrace{\text{sign}\left(\psi_b(W_i, \hat{\eta})\right)}_{\text{label}} \underbrace{\lvert \psi_b(W_i, \hat{\eta}) \rvert} _{\text{weight}}.$

## Policy Learning with Trees

• Policy trees can be fitted via the policy_tree() method

• As the tree is based on the scores, the learners do not have to be re-estimated

features = dml_data.data[["age", "marr"]]
policy_tree = dml_irm.policy_tree(features=features)

policy_tree.plot_tree()

## Policy Learning with Trees

#### Recommendation

• Evaluation of learned policies should be performed on a separate test set

• Shallow trees are recommended for policy learning

• Policy tree implementation is an approximation; a formal framework is provided in Athey and Wager (2021)

# Outlook: Quantile Treatment Effects

## Quantile Treatement Effects (QTEs)

• QTEs (from Kallus, Mao, and Uehara (2019)) are implemented via a separate class
from doubleml import DoubleMLQTE
from lightgbm import LGBMClassifier, LGBMRegressor
from sklearn.base import clone

tau_vec = np.arange(0.1,0.95,0.1)
n_folds = 5

# Learners
class_learner = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)

np.random.seed(42)
dml_QTE = DoubleMLQTE(dml_data, ml_g=clone(class_learner), ml_m=clone(class_learner),
quantiles=tau_vec, score='PQ', normalize_ipw=True)
_ = dml_QTE.fit()
print(dml_QTE)
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 265, number of negative: 2228
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.106298 -> initscore=-2.129130
[LightGBM] [Info] Start training from score -2.129130
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000704 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 266, number of negative: 2227
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.106699 -> initscore=-2.124914
[LightGBM] [Info] Start training from score -2.124914
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 250, number of negative: 2243
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.100281 -> initscore=-2.194109
[LightGBM] [Info] Start training from score -2.194109
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 253, number of negative: 2241
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.101443 -> initscore=-2.181288
[LightGBM] [Info] Start training from score -2.181288
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000646 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 258, number of negative: 2236
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.103448 -> initscore=-2.159484
[LightGBM] [Info] Start training from score -2.159484
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 139, number of negative: 1334
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.094365 -> initscore=-2.261463
[LightGBM] [Info] Start training from score -2.261463
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 158, number of negative: 1315
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.107264 -> initscore=-2.118997
[LightGBM] [Info] Start training from score -2.118997
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 121, number of negative: 1352
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000150 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.082145 -> initscore=-2.413550
[LightGBM] [Info] Start training from score -2.413550
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Warning] Contains only one class
[LightGBM] [Info] Number of positive: 0, number of negative: 1472
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776
[LightGBM] [Info] Start training from score -34.538776
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000574 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1, number of negative: 1471
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000679 -> initscore=-7.293698
[LightGBM] [Info] Start training from score -7.293698
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 552, number of negative: 1941
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.221420 -> initscore=-1.257411
[LightGBM] [Info] Start training from score -1.257411
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000998 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 552, number of negative: 1941
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.221420 -> initscore=-1.257411
[LightGBM] [Info] Start training from score -1.257411
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000618 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 536, number of negative: 1957
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.215002 -> initscore=-1.295034
[LightGBM] [Info] Start training from score -1.295034
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000697 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 542, number of negative: 1952
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.217322 -> initscore=-1.281344
[LightGBM] [Info] Start training from score -1.281344
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 522, number of negative: 1972
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.209302 -> initscore=-1.329136
[LightGBM] [Info] Start training from score -1.329136
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000786 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 268, number of negative: 1205
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.181942 -> initscore=-1.503248
[LightGBM] [Info] Start training from score -1.503248
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 261, number of negative: 1212
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.177189 -> initscore=-1.535507
[LightGBM] [Info] Start training from score -1.535507
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 219, number of negative: 1254
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.148676 -> initscore=-1.745022
[LightGBM] [Info] Start training from score -1.745022
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000768 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Warning] Contains only one class
[LightGBM] [Info] Number of positive: 0, number of negative: 1472
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776
[LightGBM] [Info] Start training from score -34.538776
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1, number of negative: 1471
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000679 -> initscore=-7.293698
[LightGBM] [Info] Start training from score -7.293698
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 798, number of negative: 1695
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.320096 -> initscore=-0.753329
[LightGBM] [Info] Start training from score -0.753329
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 792, number of negative: 1701
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.317690 -> initscore=-0.764410
[LightGBM] [Info] Start training from score -0.764410
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 789, number of negative: 1704
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.316486 -> initscore=-0.769967
[LightGBM] [Info] Start training from score -0.769967
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 794, number of negative: 1700
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.318364 -> initscore=-0.761300
[LightGBM] [Info] Start training from score -0.761300
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 831, number of negative: 1663
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333200 -> initscore=-0.693749
[LightGBM] [Info] Start training from score -0.693749
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000654 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 389, number of negative: 1084
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.264087 -> initscore=-1.024834
[LightGBM] [Info] Start training from score -1.024834
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000638 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 363, number of negative: 1110
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.246436 -> initscore=-1.117712
[LightGBM] [Info] Start training from score -1.117712
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 335, number of negative: 1138
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000150 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.227427 -> initscore=-1.222897
[LightGBM] [Info] Start training from score -1.222897
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Warning] Contains only one class
[LightGBM] [Info] Number of positive: 0, number of negative: 1472
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000164 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776
[LightGBM] [Info] Start training from score -34.538776
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000615 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1, number of negative: 1471
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000142 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000679 -> initscore=-7.293698
[LightGBM] [Info] Start training from score -7.293698
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1148, number of negative: 1345
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.460489 -> initscore=-0.158373
[LightGBM] [Info] Start training from score -0.158373
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1153, number of negative: 1340
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.462495 -> initscore=-0.150302
[LightGBM] [Info] Start training from score -0.150302
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1153, number of negative: 1340
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.462495 -> initscore=-0.150302
[LightGBM] [Info] Start training from score -0.150302
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000762 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1157, number of negative: 1337
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.463913 -> initscore=-0.144598
[LightGBM] [Info] Start training from score -0.144598
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1149, number of negative: 1345
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.460706 -> initscore=-0.157502
[LightGBM] [Info] Start training from score -0.157502
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000611 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 475, number of negative: 998
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.322471 -> initscore=-0.742438
[LightGBM] [Info] Start training from score -0.742438
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 428, number of negative: 1045
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.290563 -> initscore=-0.892649
[LightGBM] [Info] Start training from score -0.892649
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 437, number of negative: 1036
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000131 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.296673 -> initscore=-0.863189
[LightGBM] [Info] Start training from score -0.863189
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001388 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Warning] Contains only one class
[LightGBM] [Info] Number of positive: 0, number of negative: 1472
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000000 -> initscore=-34.538776
[LightGBM] [Info] Start training from score -34.538776
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1, number of negative: 1471
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000806 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.000679 -> initscore=-7.293698
[LightGBM] [Info] Start training from score -7.293698
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000875 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1399, number of negative: 1094
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.561171 -> initscore=0.245917
[LightGBM] [Info] Start training from score 0.245917
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1389, number of negative: 1104
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.557160 -> initscore=0.229644
[LightGBM] [Info] Start training from score 0.229644
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1433, number of negative: 1060
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.574809 -> initscore=0.301501
[LightGBM] [Info] Start training from score 0.301501
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1400, number of negative: 1094
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.561347 -> initscore=0.246632
[LightGBM] [Info] Start training from score 0.246632
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1442, number of negative: 1052
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.578188 -> initscore=0.315338
[LightGBM] [Info] Start training from score 0.315338
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 602, number of negative: 871
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.408690 -> initscore=-0.369385
[LightGBM] [Info] Start training from score -0.369385
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 552, number of negative: 921
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.374745 -> initscore=-0.511912
[LightGBM] [Info] Start training from score -0.511912
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 584, number of negative: 889
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.396470 -> initscore=-0.420196
[LightGBM] [Info] Start training from score -0.420196
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 570, number of negative: 902
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.387228 -> initscore=-0.458978
[LightGBM] [Info] Start training from score -0.458978
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 555, number of negative: 917
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000138 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.377038 -> initscore=-0.502139
[LightGBM] [Info] Start training from score -0.502139
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1656, number of negative: 837
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.664260 -> initscore=0.682336
[LightGBM] [Info] Start training from score 0.682336
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000766 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1623, number of negative: 870
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.651023 -> initscore=0.623538
[LightGBM] [Info] Start training from score 0.623538
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1647, number of negative: 846
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.660650 -> initscore=0.666191
[LightGBM] [Info] Start training from score 0.666191
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1690, number of negative: 804
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.677626 -> initscore=0.742885
[LightGBM] [Info] Start training from score 0.742885
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1685, number of negative: 809
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.675621 -> initscore=0.733722
[LightGBM] [Info] Start training from score 0.733722
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000598 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 752, number of negative: 721
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.510523 -> initscore=0.042097
[LightGBM] [Info] Start training from score 0.042097
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 802, number of negative: 671
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544467 -> initscore=0.178339
[LightGBM] [Info] Start training from score 0.178339
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000695 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 726, number of negative: 747
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492872 -> initscore=-0.028515
[LightGBM] [Info] Start training from score -0.028515
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000657 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 704, number of negative: 768
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.478261 -> initscore=-0.087011
[LightGBM] [Info] Start training from score -0.087011
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 698, number of negative: 774
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000144 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.474185 -> initscore=-0.103353
[LightGBM] [Info] Start training from score -0.103353
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1891, number of negative: 602
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.758524 -> initscore=1.144604
[LightGBM] [Info] Start training from score 1.144604
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1860, number of negative: 633
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.746089 -> initscore=1.077861
[LightGBM] [Info] Start training from score 1.077861
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1917, number of negative: 576
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.768953 -> initscore=1.202409
[LightGBM] [Info] Start training from score 1.202409
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1902, number of negative: 592
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.762630 -> initscore=1.167155
[LightGBM] [Info] Start training from score 1.167155
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000570 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1947, number of negative: 547
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.780674 -> initscore=1.269596
[LightGBM] [Info] Start training from score 1.269596
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 907, number of negative: 566
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.615750 -> initscore=0.471548
[LightGBM] [Info] Start training from score 0.471548
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000694 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 874, number of negative: 599
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.593347 -> initscore=0.377819
[LightGBM] [Info] Start training from score 0.377819
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 897, number of negative: 576
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.608961 -> initscore=0.442948
[LightGBM] [Info] Start training from score 0.442948
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 879, number of negative: 593
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.597147 -> initscore=0.393590
[LightGBM] [Info] Start training from score 0.393590
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 882, number of negative: 590
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.599185 -> initscore=0.402070
[LightGBM] [Info] Start training from score 0.402070
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2113, number of negative: 380
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.847573 -> initscore=1.715693
[LightGBM] [Info] Start training from score 1.715693
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2093, number of negative: 400
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.839551 -> initscore=1.654889
[LightGBM] [Info] Start training from score 1.654889
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2120, number of negative: 373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.850381 -> initscore=1.737593
[LightGBM] [Info] Start training from score 1.737593
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2103, number of negative: 391
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.843224 -> initscore=1.682413
[LightGBM] [Info] Start training from score 1.682413
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2125, number of negative: 369
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.852045 -> initscore=1.750730
[LightGBM] [Info] Start training from score 1.750730
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1095, number of negative: 378
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.743381 -> initscore=1.063615
[LightGBM] [Info] Start training from score 1.063615
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000518 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1045, number of negative: 428
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.709437 -> initscore=0.892649
[LightGBM] [Info] Start training from score 0.892649
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1068, number of negative: 405
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.725051 -> initscore=0.969656
[LightGBM] [Info] Start training from score 0.969656
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1078, number of negative: 394
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.732337 -> initscore=1.006512
[LightGBM] [Info] Start training from score 1.006512
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1084, number of negative: 388
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.736413 -> initscore=1.027408
[LightGBM] [Info] Start training from score 1.027408
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000622 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2332, number of negative: 161
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.935419 -> initscore=2.673077
[LightGBM] [Info] Start training from score 2.673077
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2300, number of negative: 193
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000628 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.922583 -> initscore=2.477974
[LightGBM] [Info] Start training from score 2.477974
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000968 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2311, number of negative: 182
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 2493, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.926996 -> initscore=2.541429
[LightGBM] [Info] Start training from score 2.541429
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000874 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000725 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2306, number of negative: 188
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.924619 -> initscore=2.506828
[LightGBM] [Info] Start training from score 2.506828
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001576 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 2321, number of negative: 173
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 2494, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.930634 -> initscore=2.596462
[LightGBM] [Info] Start training from score 2.596462
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000705 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1280, number of negative: 193
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.868975 -> initscore=1.891925
[LightGBM] [Info] Start training from score 1.891925
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set force_col_wise=true to remove the overhead.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 336
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1248, number of negative: 225
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.847251 -> initscore=1.713197
[LightGBM] [Info] Start training from score 1.713197
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1256, number of negative: 217
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 332
[LightGBM] [Info] Number of data points in the train set: 1473, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.852682 -> initscore=1.755790
[LightGBM] [Info] Start training from score 1.755790
[LightGBM] [Info] Number of positive: 2946, number of negative: 4986
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000615 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 339
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371407 -> initscore=-0.526186
[LightGBM] [Info] Start training from score -0.526186
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000370 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 337
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1270, number of negative: 202
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 330
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.862772 -> initscore=1.838504
[LightGBM] [Info] Start training from score 1.838504
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001286 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
[LightGBM] [Info] Number of positive: 1178, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3172, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371375 -> initscore=-0.526325
[LightGBM] [Info] Start training from score -0.526325
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1179, number of negative: 1994
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000837 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371573 -> initscore=-0.525476
[LightGBM] [Info] Start training from score -0.525476
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1178, number of negative: 1995
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 335
[LightGBM] [Info] Number of data points in the train set: 3173, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371257 -> initscore=-0.526826
[LightGBM] [Info] Start training from score -0.526826
[LightGBM] [Info] Number of positive: 1261, number of negative: 211
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 333
[LightGBM] [Info] Number of data points in the train set: 1472, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.856658 -> initscore=1.787802
[LightGBM] [Info] Start training from score 1.787802
[LightGBM] [Info] Number of positive: 2945, number of negative: 4987
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set force_row_wise=true to remove the overhead.
And if memory is not enough, you can set force_col_wise=true.
[LightGBM] [Info] Total Bins 338
[LightGBM] [Info] Number of data points in the train set: 7932, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.371281 -> initscore=-0.526726
[LightGBM] [Info] Start training from score -0.526726
================== DoubleMLQTE Object ==================

------------------ Fit summary       ------------------
coef      std err          t         P>|t|         2.5 %        97.5 %
0.1   1210.0   486.438569   2.487467  1.286563e-02    256.597923   2163.402077
0.2   1211.0   252.052811   4.804549  1.551010e-06    716.985569   1705.014431
0.3    622.0   255.252133   2.436806  1.481761e-02    121.715013   1122.284987
0.4   2006.0   320.163566   6.265547  3.715180e-10   1378.490941   2633.509059
0.5   4601.0   448.109454  10.267581  9.864741e-25   3722.721609   5479.278391
0.6   7040.0   605.739720  11.622153  3.180176e-31   5852.771965   8227.228035
0.7  10928.0   859.705581  12.711328  5.115792e-37   9243.008023  12612.991977
0.8  16590.0  1589.396531  10.437924  1.664103e-25  13474.840041  19705.159959
0.9  21550.0  2279.055439   9.455672  3.209546e-21  17083.133421  26016.866579

## Quantile Treatement Effects (QTEs)

• Create simultaneously valid confidence intervals
_ = dml_QTE.bootstrap(n_rep_boot=2000)
ci_QTE = dml_QTE.confint(level=0.95, joint=True)

print(ci_QTE)
            2.5 %        97.5 %
0.1    -97.964554   2517.964554
0.2    533.265620   1888.734380
0.3    -64.336904   1308.336904
0.4   1145.125426   2866.874574
0.5   3396.097019   5805.902981
0.6   5411.251515   8668.748485
0.7   8616.373212  13239.626788
0.8  12316.337390  20863.662610
0.9  15421.942072  27678.057928

## Quantile Treatement Effects (QTEs)

Code
ci_QTE_pointwise = dml_QTE.confint(level=0.95, joint=False)

data_qte = {"Quantile": tau_vec, "DML QTE": dml_QTE.coef,
"DML QTE lower": ci_QTE["2.5 %"], "DML QTE upper": ci_QTE["97.5 %"],
"DML QTE lower pointwise": ci_QTE_pointwise["2.5 %"],
"DML QTE upper pointwise": ci_QTE_pointwise["97.5 %"]}
df_qte = pd.DataFrame(data_qte)

fig, ax = plt.subplots()
_ = ax.grid(visible=True)

_ = ax.plot(df_qte['Quantile'],df_qte['DML QTE'], color='violet', label='Estimated QTE')
_ = ax.fill_between(df_qte['Quantile'], df_qte['DML QTE lower'], df_qte['DML QTE upper'], color='violet', alpha=.3, label='Joint Confidence Interval')
_ = ax.fill_between(df_qte['Quantile'], df_qte['DML QTE lower pointwise'], df_qte['DML QTE upper pointwise'], color='violet', alpha=.5, label='Pointwise Confidence Interval')
ci_QTE_pointwise

_ = plt.legend()
_ = plt.title('Quantile Treatment Effects', fontsize=16)
_ = plt.xlabel('Quantile')
_ = plt.ylabel('QTE and 95%-CI')
plt.show()

# References

## References

Athey, Susan, and Stefan Wager. 2021. “Policy Learning with Observational Data.” Econometrica 89 (1): 133–61.
Bach, Philipp, Victor Chernozhukov, Malte S Kurz, and Martin Spindler. 2022. “DoubleML-an Object-Oriented Implementation of Double Machine Learning in Python.” Journal of Machine Learning Research 23: 53–51.
Bach, Philipp, Victor Chernozhukov, Malte S Kurz, Martin Spindler, and Sven Klaassen. 2021. DoubleMLAn Object-Oriented Implementation of Double Machine Learning in R.” https://arxiv.org/abs/2103.09603.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. forthcoming. Applied Causal Inference Powered by ML and AI. online.
Kallus, Nathan, Xiaojie Mao, and Masatoshi Uehara. 2019. “Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond.” arXiv Preprint arXiv:1912.12945.
Semenova, Vira, and Victor Chernozhukov. 2021. “Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions.” The Econometrics Journal 24 (2): 264–89.